基于RSSI-APIT算法的室内定位系统
Xiaoyan Shen1,2, Boyang Xu1,2, Hongming Shen1
1School of Information Science and Technology, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了RSSI-APIT算法,通过将接收信号强度指示 (RSSI) 和近似完美的点在三角测试 (APIT) 与机器学习相融合,提高室内定位的准确性. 改进的算法大大减少了复杂环境中的本地化错误.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 地理学工程 工程地质学
背景情况:
- 室内定位系统由于信号波动和多路径效应而面临准确性挑战.
- 像APIT这样的传统方法在广泛的场景中难以准确.
- 机器学习集成是克服当前室内定位技术局限性的关键.
研究的目的:
- 通过改进数据预处理和本地化算法,开发一个增强的室内本地化系统.
- 使用高斯过和人工神经网络 (ANN) 减轻RSSI波动和多路径效应.
- 通过一种新的RSSI-APIT算法,提高室内定位的准确性和稳定性.
主要方法:
- 接收信号强度指示 (RSSI) 和近似完美点在三角测试 (APIT) 定位方法的融合.
- 整合高斯过和人工神经网络 (ANN) 进行RSSI数据预处理.
- 将RSSI范围功能纳入APIT,以提高广域定位精度.
主要成果:
- 与三边定位相比,RSSI-APIT算法成功地减少了约2.9米的本地化误差,与传统APIT相比,减少了1.8米.
- 在100平方米的复杂环境中,定位误差在1.55米以内被始终控制.
- 该系统证明了号呼叫频率的减少,从而降低了运营成本,提高了本地化准确性和稳定性.
结论:
- RSSI-APIT算法在室内定位准确性和稳定性方面提供了显著的改进.
- 先进的信号处理和机器学习的整合有效地解决了多路径效应,并提高了定位精度.
- 这种增强的系统提供了一个具有成本效益和可靠的解决方案,用于在各种环境中精确的室内定位.
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